The human lymphatic system is commonly affected by two primary forms of lymphoma disease: Hodgkin lymphoma and non-Hodgkin lymphoma. The second type, in particular, has emerged as a leading cause of patient mortality. Therefore, achieving a correct and early diagnosis is crucial for healthcare practitioners to devise suitable therapeutic strategies. For these reasons, in this work, We suggest the development of a computer-aided diagnosis system utilizing a novel hybrid approach that incorporates Handcrafted features and BiLSTM networks for the discrimination and analysis of patients with evolving lymphoma from those with residual masses who do not need re-treatment. Our proposed approach combines the concatenation of all extracted features obtained through various handcrafted methods (including histogram analysis, textural, and shape analysis) to analyze the functional, morphological, and anatomical aspects of each lesion. The "LWBDWMRI" databases were utilised for the experiment. We compared the experimental results of the suggested approach to each model: BiLSTM, LSTM, and VGG16. This comparison was conducted across five different approach cases: concatenating functional features only, textural features only, morphological features only, combining textural and morphological features to obtain global anatomical features, and incorporating both functional and anatomical criteria. The proposed approach achieved 96%, 97%, 98%, and 26.11 seconds for Accuracy, F1-score, Recall, and execution time, respectively.

Human voice is an ideal data source for identifying people in many applications. Because of the increasing need for security in different public places, voice biometrics may be a good solution, as we can easily take voice records. This paper provides a brief overview of the approaches utilized in recognizing speakers, and then presents a novel approach for recognizing speakers in degraded smart-home conditions. The suggested approach includes a pre-processing phase, a feature extraction phase, and a classification phase, where the feature extraction phase consists of formant extraction to get the spectrum energy maxima of speech audio, dynamic time warping (DTW)to find an optimal alignment between two provided temporal sequences under definite restrictions, and refinement process to improve the results of the DTW system output. The experiments are carried out on a database containing 1,248 samples in order to validate the suggested approach. The latter has good results as regards the state of the art with 94.5% accuracy.

Since the smartphone is adapted to the ambient intelligence and the smart home systems are remotely accessed through the smartphones, there is a need for a secure authentication system based on some biometrics proprieties that can be taken from a smartphone. The identification of persons through ear and voice print is one of the basic biometric matters. The earlier research in ear recognition have shown that human ear is one of the representative human biometrics with uniqueness and stability. Indeed, the human voice is a perfect source of data for person identification in many applications. In this paper, we propose a fusion between the ear and voice biometrics in degraded conditions in a smart home context at 3 levels (feature, score, and decision). The experiments are conducted on the EVDDC database and a chimeric database (TIMIT and USTB-I). The best results are obtained with the feature level fusion (95.8%) with the KNN classifier.

Lymphoma affects the human lymphatic system, it has become one of the leading causes of patient deaths. Hence, accurate diagnosis is essential to help doctors prescribe a suitable treatment. In this work, we propose a new database that contains 50 volunteer patients treated for lymphoma on Whole Body (head/neck, chest, abdomen, and pelvis regions). These collected databases contain some MRI sequences and medical information of patients which tells whether that lesion is evolutive lymphoma or residual masses. It is mainly composed of 100000 images on axial, coronal, and sagittal plans. To highlight the utility of this dataset, we used a computer-aided diagnosis (CAD) system to recognize evolutive lymphoma from residual masses. This recognition is very important from a medical point of view, as it helps identify patients who may need additional therapy. After successful steps of database preparation, features extraction and selection, we evaluated several Machine Learning models such as Random Forest, Decision Tree, Naive Bayes, Extreme Gradient Boost, Logistic Regression, K-Nearest Neighbors (K-NN), and Support Vector Machine (SVM). The metrics of Accuracy, Precision, Sensitivity, Specificity, confusion matrix, Positive Predictive Value, Negative Predictive Value, Fl-score, missed classification, and Recall was measured to evaluate our CAD system based on AI models. The best obtained results reach 95% accuracy for SVM with RBF kernel. In addition, the proposed approach was compared to three literature works, and it gives much better results that can correctly recognize more than 64 lesions out of 67 cases. Interested researchers could contact the author to acquire the database.

With the start of 2020, the world witnessed the spread of Coronavirus disease (COVID-19). We aim in this work to employ artificial intelligence (AI) to develop a computer-aided diagnosis system (CAD) in order to automatically detect COVID-19 cases and differentiate them from normal and community-acquired pneumonia (CAP) cases through the use of lung Computed Tomography (CT) images and then evaluate its performance. Deep residual learning offers a wide variety of algorithms that helps in classification problems. We apply in this work a ResNet50 based model to recognize Covid-19 cases. Extensive analysis based on an international dataset (24256 images of 304 patients) proved that the ResNet50-optimized model can recognize COVID-19 through the use of CT images with 82% accuracy, 90% recall, 65% precision, and 76% of F1.Score.

Detecting lymphoma lesions in the whole body is tedious and so much time consuming. In this paper, we propose a semi-automatic lymphoma lesion detection based on Chan-Vese algorithm to help doctors in their diagnosis. In addition, this study will helps doctors in the ADC measurement of the lymph nodes. The proposed algorithm is applied on real DW-MRI images obtained from 1.5T MR scan. To evaluate the proposed method, we compared the obtained results with those obtained with the region growing algorithm (RG). This evaluation is basically based on: sensitivity, specificity, accuracy, precision, and recall. The obtained values for these parameters confirm the efficiency of the proposed method especially for the accuracy which reaches 99.94% with query times.